Document Type
Article
Publication Date
4-1-2024
Original Citation
Sheehan S,
Mawe S,
Chen M,
Klug J,
Ladiges W,
Korstanje R,
Mahoney J.
A machine learning approach for quantifying age-related histological changes in the mouse kidney. Geroscience. 2024;46(2):2571-81
Keywords
JMG, Mice, Animals, Machine Learning, Aging, Kidney
JAX Source
Geroscience. 2024;46(2):2571-81
ISSN
2509-2723
PMID
38103095
DOI
https://doi.org/10.1007/s11357-023-01013-y
Grant
SS and RK are supported by grants from the National Institutes of Health (AG022308, AG038070, DK131019, and DK131061), JMM is supported by AG038070 and GM141309. SM supported by GM141309.
Abstract
The ability to quantify aging-related changes in histological samples is important, as it allows for evaluation of interventions intended to effect health span. We used a machine learning architecture that can be trained to detect and quantify these changes in the mouse kidney. Using additional held out data, we show validation of our model, correlation with scores given by pathologists using the Geropathology Research Network aging grading scheme, and its application in providing reproducible and quantifiable age scores for histological samples. Aging quantification also provides the insights into possible changes in image appearance that are independent of specific geropathology-specified lesions. Furthermore, we provide trained classifiers for H&E-stained slides, as well as tutorials on how to use these and how to create additional classifiers for other histological stains and tissues using our architecture. This architecture and combined resources allow for the high throughput quantification of mouse aging studies in general and specifically applicable to kidney tissues.
Comments
Open Access This article is licensed under a Creative Com- mons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Crea- tive Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.